Welcome to the InfoSci Platform
Reference Hub6
Supercritical Pitchfork Bifurcation in Implicit Regression Modeling

Supercritical Pitchfork Bifurcation in Implicit Regression Modeling

Stan Lipovetsky
Copyright: © 2010 |Volume: 1 |Issue: 4 |Pages: 9
ISSN: 1947-3087|EISSN: 1947-3079|EISBN13: 9781613502709|DOI: 10.4018/jalr.2010100101
Cite Article Cite Article

MLA

Lipovetsky, Stan. "Supercritical Pitchfork Bifurcation in Implicit Regression Modeling." IJALR vol.1, no.4 2010: pp.1-9. http://doi.org/10.4018/jalr.2010100101

APA

Lipovetsky, S. (2010). Supercritical Pitchfork Bifurcation in Implicit Regression Modeling. International Journal of Artificial Life Research (IJALR), 1(4), 1-9. http://doi.org/10.4018/jalr.2010100101

Chicago

Lipovetsky, Stan. "Supercritical Pitchfork Bifurcation in Implicit Regression Modeling," International Journal of Artificial Life Research (IJALR) 1, no.4: 1-9. http://doi.org/10.4018/jalr.2010100101

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

Chaotic systems have been widely studied for description and explanation of various observed phenomena. The problem of statistical modeling for messy data can be attempted using the so called Supercritical Pitchfork Bifurcation (SPB) approach. This work considers the possibility of applying SPB technique to regression modeling of the implicit functions. Theoretical and practical advantages of SPB regression are discussed with an example from marketing research data on advertising in the car industry. Results are very promising, which can help in modeling, analysis, interpretation, and lead to understanding of the real world data.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.